Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail

Auriel M.V. Fournier, Alexis R. Sullivan, Joseph K. Bump, Marie Perkins, Mark C. Shieldcastle, Sammy L. King

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Stable hydrogen isotope (δD) methods for tracking animal movement are widely used yet often produce low resolution assignments. Incorporating prior knowledge of abundance, distribution or movement patterns can ameliorate this limitation, but data are lacking for most species. We demonstrate how observations reported by citizen scientists can be used to develop robust estimates of species distributions and to constrain δD assignments. We developed a Bayesian framework to refine isotopic estimates of migrant animal origins conditional on species distribution models constructed from citizen scientist observations. To illustrate this approach, we analysed the migratory connectivity of the Virginia rail Rallus limicola, a secretive and declining migratory game bird in North America. Citizen science observations enabled both estimation of sampling bias and construction of bias-corrected species distribution models. Conditioning δD assignments on these species distribution models yielded comparably high-resolution assignments. Most Virginia rails wintering across five Gulf Coast sites spent the previous summer near the Great Lakes, although a considerable minority originated from the Chesapeake Bay watershed or Prairie Pothole region of North Dakota. Conversely, the majority of migrating Virginia rails from a site in the Great Lakes most likely spent the previous winter on the Gulf Coast between Texas and Louisiana. Synthesis and applications. In this analysis, Virginia rail migratory connectivity does not fully correspond to the administrative flyways used to manage migratory birds. This example demonstrates that with the increasing availability of citizen science data to create species distribution models, our framework can produce high-resolution estimates of migratory connectivity for many animals, including cryptic species. Empirical evidence of links between seasonal habitats will help enable effective habitat management, hunting quotas and population monitoring and also highlight critical knowledge gaps.

Original languageEnglish (US)
Pages (from-to)618-627
Number of pages10
JournalJournal of Applied Ecology
Issue number2
StatePublished - Apr 1 2017

Bibliographical note

Funding Information:
We are grateful to Shawn O'Neil and an anonymous reviewer for constructive comments on earlier drafts of the manuscript. Thanks to the Ecosystem Science Center at Michigan Technological University for funding part of this project through their undergraduate research programme. We thank the following museums for access to specimens: The Sam Noble Oklahoma Museum of Natural History; The Field Museum of Natural History, Chicago; University of Kansas Natural History Museum; Ornithology Department, Museum of Comparative Zoology, Harvard. The Louisiana Department of Wildlife and Fisheries provided support for the Gulf Coast samples through the Rockefeller Wildlife Refuge. This research was funded in part by the National Science Foundation (DEB Proposal 1545611 to JKB). Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Publisher Copyright:
© 2016 The Authors. Journal of Applied Ecology © 2016 British Ecological Society


  • Bayesian
  • Virginia rail Rallus limicola
  • citizen science
  • eBird
  • feathers
  • hydrogen isotopes
  • migration
  • migratory connectivity
  • species distribution model
  • δD animal origins


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